1.4k citations · 5.9k across the 68 of their papers we have counts for
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Improving EEG Decoding via Clustering-based Multi-task Feature Learning
Yu Zhang, Tao Zhou, Wei Wu +4
Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challen…
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
Shu Gong, Kaibo Xing, Andrzej Cichocki +1
Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but t…
Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network
Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin +6
Most state of the art deep neural networks are overparameterized and exhibit a high computational cost. A straightforward approach to this problem is to replace convolutional kerne…
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles
Andrzej Cichocki, Alexander P. Kuleshov
This article discusses some trends and concepts in developing new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different ty…
Towards Understanding Normalization in Neural ODEs
Julia Gusak, Larisa Markeeva, Talgat Daulbaev +3
Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly…
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva +3
We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models. We compare it with the reverse dynamic method (known in the litera…